Airborne multispectral identification of individual cotton plants using consumer-grade cameras

Remote Sensing Applications: Society and Environment

Volumn 4, Issue , 2016, Pages 37-43

  Westbrook, John K ^a   Eyster, Ritchie S ^a   Yang, Chenghai ^a   Suh, Charles P C ^a  

^a AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

Insect pest management; Linear spectral unmixing; Regrowth cotton; Remote sensing; Volunteer cotton

Indexed keywords

EID: 84973517704     PISSN: None     EISSN: 23529385     Source Type: Journal    
DOI: 10.1016/j.rsase.2016.02.002     Document Type: Article

References (9)

1. Bai, J., Li, J., Li, S., 2011. Monitoring the plant density of cotton with remotely sensed data. In: Computer and Computing Technologies in Agriculture IV, Li, D., Liu, Y., Chen, Y. (Eds.), Advances in Information and Communication Technology 345, International Federation for Information Processing, Beijing, China, pp. 90-101 〈〉. http://dx.doi.org/10.1007/978-3-642-18336-2.
2. Summy K.R., Heilman M.D., Namken L.N., Hart W.G. Control of boll weevils (Coleoptera: Curculionidae) through crop residue disposal: Destruction of subtropical cotton under inclement conditions. J. Econ. Entomol. 1986, 79:1662-1665. http://dx.doi.org/10.1093/jee/79.6.1662.
3. Troxclair, N., 2010. Unique situations and challenges for the Texas Boll Weevil Eradication Program in the Winter Garden area of Texas. National Cotton Council, Memphis, TN. Proc. Beltwide Cotton Conf., pp. 1024-1026.
4. Westbrook J.K., Suh C.P.-C., Yang C., Lan Y., Eyster R.S. Airborne multispectral detection of regrowth cotton fields. J. Appl. Remote. Sens. 2015, 9(096081):1-11. http://dx.doi.org/10.1117/1.JRS.9.096081.
5. Yang C., Everitt J.H., Bradford J.M. Using multispectral imagery and linear spectral unmixing techniques for estimating crop yield variability. Trans. ASABE 2007, 50:667-674. http://dx.doi.org/10.13031/2013.22658.
6. Yang C., Greenberg S.M., Everitt J.H., Fernandez C.J. Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology. Int. J. Agric. Biol. Eng. 2011, 4(4):1-11.
7. Yang C., Westbrook J.K., Suh C.P.-C., Martin D.E., Hoffmann W.C., Lan Y., Fritz B.K., Goolsby J.A. An airborne multispectral imaging system based on two consumer-grade cameras for agricultural remote sensing. Remote. Sens. 2014, 6:5257-5278. http://dx.doi.org/10.3390/rs6065257.
8. Zhang H., Lan Y., Suh C.P., Westbrook J.K., Lacey R., Hoffmann W.C. Differentiation of cotton from other crops at different growth stages using spectral properties and discriminant analysis. Trans. ASABE 2012, 55:1623-1630. http://dx.doi.org/10.13031/2013.42229.
9. Zhang H., Lan Y., Suh C.P., Westbrook J.K., Hoffmann W.C., Yang C.H., Huang Y.B. Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants. Comput. Electron. Agric. 2013, 93:55-59. http://dx.doi.org/10.1016/j.compag.2013.02.001.